OpenRaiser/Intern-Atlas huggingface dataset:
On the evaluation of research software: the CDUR procedure
Venue: arXiv cs.SE 2021
Year: 2021

Background: Evaluation of the quality of research software is a challenging and relevant issue, still not sufficiently addressed by the scientific community. Methods: Our contribution begins by defining, precisely but widely enough, the notions of research software and of its authors followed by a study of the evaluation issues, as the basis for the proposition of a sound assessment protocol: the CDUR procedure. Results: CDUR comprises four steps introduced as follows: Citation, to deal with correct RS identification, Dissemination, to measure good dissemination practices, Use, devoted to the evaluation of usability aspects, and Research, to assess the impact of the scientific work. Conclusions: Some conclusions and recommendations are finally included. The evaluation of research is the keystone to boost the evolution of the Open Science policies and practices. It is as well our belief that research software evaluation is a fundamental step to induce better research software practices and, thus, a step towards more efficient science.
5G NB-IoT via low density LEO Constellations
Venue: arXiv cs.NI 2021
Year: 2021

5G NB-IoT is seen as a key technology for providing truly ubiquitous, global 5G coverage (1.000.000 devices/km2) for machine type communications in the internet of things. A non-terrestrial network (NTN) variant of NB-IoT is being standardized in the 3GPP, which along with inexpensive and non-complex chip-sets enables the production of competitively priced IoT devices with truly global coverage. NB-IoT allows for narrowband single carrier transmissions in the uplink, which improves the uplink link-budget by as much as 16.8 dB over the 180 [kHz] downlink. This allows for a long range sufficient for ground to low earth orbit (LEO) communication without the need for complex and expensive antennas in the IoT devices. In this paper the feasibility of 5G NB-IoT in the context of low-density constellations of small-satellites carrying base-stations in LEO is analyzed and required adaptations to NB-IoT are discussed.
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence
Venue: arXiv cs.IR 2021
Year: 2021

The growing number of AI applications, also for high-stake decisions, increases the interest in Explainable and Interpretable Machine Learning (XI-ML). This trend can be seen both in the increasing number of regulations and strategies for developing trustworthy AI and the growing number of scientific papers dedicated to this topic. To ensure the sustainable development of AI, it is essential to understand the dynamics of the impact of regulation on research papers as well as the impact of scientific discourse on AI-related policies. This paper introduces a novel framework for joint analysis of AI-related policy documents and eXplainable Artificial Intelligence (XAI) research papers. The collected documents are enriched with metadata and interconnections, using various NLP methods combined with a methodology inspired by Institutional Grammar. Based on the information extracted from collected documents, we showcase a series of analyses that help understand interactions, similarities, and differences between documents at different stages of institutionalization. To the best of our knowledge, this is the first work to use automatic language analysis tools to understand the dynamics between XI-ML methods and regulations. We believe that such a system contributes to better cooperation between XAI researchers and AI policymakers.
Jasmine: A New Active Learning Approach to Combat Cybercrime
Venue: arXiv cs.CR 2021
Year: 2021

Over the past decade, the advent of cybercrime has accelarated the research on cybersecurity. However, the deployment of intrusion detection methods falls short. One of the reasons for this is the lack of realistic evaluation datasets, which makes it a challenge to develop techniques and compare them. This is caused by the large amounts of effort it takes for a cyber analyst to classify network connections. This has raised the need for methods (i) that can learn from small sets of labeled data, (ii) that can make predictions on large sets of unlabeled data, and (iii) that request the label of only specially selected unlabeled data instances. Hence, Active Learning (AL) methods are of interest. These approaches choose specific unlabeled instances by a query function that are expected to improve overall classification performance. The resulting query observations are labeled by a human expert and added to the labeled set. In this paper, we propose a new hybrid AL method called Jasmine. Firstly, it determines how suitable each observation is for querying, i.e., how likely it is to enhance classification. These properties are the uncertainty score and anomaly score. Secondly, Jasmine introduces dynamic updating. This allows the model to adjust the balance between querying uncertain, anomalous and randomly selected observations. To this end, Jasmine is able to learn the best query strategy during the labeling process. This is in contrast to the other AL methods in cybersecurity that all have static, predetermined query functions. We show that dynamic updating, and therefore Jasmine, is able to consistently obtain good and more robust results than querying only uncertainties, only anomalies or a fixed combination of the two.
Contrasting Third-Party Package Management User Experience
Venue: arXiv cs.SE 2021
Year: 2021

The management of third-party package dependencies is crucial to most technology stacks, with package managers acting as brokers to ensure that a verified package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of package ecosystems with their own management features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that package ecosystems are used without any issue. In this study, we explore 13 package ecosystems to understand whether their features correlate with the experience of their users. By studying experience through the questions that developers ask on the question-and-answer site Stack Overflow, we find that developer questions are grouped into three themes (i.e., Package management, Input-Output, and Package Usage). Our preliminary analysis indicates that specific features are correlated with the user experience. Our work lays out future directions to investigate the trade-offs involved in designing the ideal package ecosystem.
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
Venue: arXiv cs.RO 2021
Year: 2021

The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities. This article provides a concise but holistic review of the recent advances made in using machine learning to achieve safe decision making under uncertainties, with a focus on unifying the language and frameworks used in control theory and reinforcement learning research. Our review includes: learning-based control approaches that safely improve performance by learning the uncertain dynamics, reinforcement learning approaches that encourage safety or robustness, and methods that can formally certify the safety of a learned control policy. As data- and learning-based robot control methods continue to gain traction, researchers must understand when and how to best leverage them in real-world scenarios where safety is imperative, such as when operating in close proximity to humans. We highlight some of the open challenges that will drive the field of robot learning in the coming years, and emphasize the need for realistic physics-based benchmarks to facilitate fair comparisons between control and reinforcement learning approaches.
Towards Structured Dynamic Sparse Pre-Training of BERT
Venue: arXiv cs.CL 2021
Year: 2021

Identifying algorithms for computational efficient unsupervised training of large language models is an important and active area of research. In this work, we develop and study a straightforward, dynamic always-sparse pre-training approach for BERT language modeling task, which leverages periodic compression steps based on magnitude pruning followed by random parameter re-allocation. This approach enables us to achieve Pareto improvements in terms of the number of floating-point operations (FLOPs) over statically sparse and dense models across a broad spectrum of network sizes. Furthermore, we demonstrate that training remains FLOP-efficient when using coarse-grained block sparsity, making it particularly promising for efficient execution on modern hardware accelerators.
3D point cloud segmentation using GIS
Venue: arXiv cs.CV 2021
Year: 2021

In this paper we propose an approach to perform semantic segmentation of 3D point cloud data by importing the geographic information from a 2D GIS layer (OpenStreetMap). The proposed automatic procedure identifies meaningful units such as buildings and adjusts their locations to achieve best fit between the GIS polygonal perimeters and the point cloud. Our processing pipeline is presented and illustrated by segmenting point cloud data of Trinity College Dublin (Ireland) campus constructed from optical imagery collected by a drone.
Spatio-Temporal Split Learning
Venue: arXiv cs.LG 2021
Year: 2021

This paper proposes a novel split learning framework with multiple end-systems in order to realize privacypreserving deep neural network computation. In conventional split learning frameworks, deep neural network computation is separated into multiple computing systems for hiding entire network architectures. In our proposed framework, multiple computing end-systems are sharing one centralized server in split learning computation, where the multiple end-systems are with input and first hidden layers and the centralized server is with the other hidden layers and output layer. This framework, which is called as spatio-temporal split learning, is spatially separated for gathering data from multiple end-systems and also temporally separated due to the nature of split learning. Our performance evaluation verifies that our proposed framework shows nearoptimal accuracy while preserving data privacy.
Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with User-centric Clustering
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we optimize user scheduling, power allocation and beamforming in distributed multiple-input multiple-output (MIMO) networks implementing user-centric clustering. We study both the coherent and non-coherent transmission modes, formulating a weighted sum rate maximization problem for each; finding the optimal solution to these problems is known to be NP-hard. We use tools from fractional programming, block coordinate descent, and compressive sensing to construct an algorithm that optimizes the beamforming weights and user scheduling and converges in a smooth non-decreasing pattern. Channel state information (CSI) being crucial for optimization, we highlight the importance of employing a low-overhead pilot assignment policy for scheduling problems. In this regard, we use a variant of hierarchical agglomerative clustering, which provides a suboptimal, but feasible, pilot assignment scheme; for our cell-free case, we formulate an area-based pilot reuse factor. Our results show that our scheme provides large gains in the long-term network sum spectral efficiency compared to benchmark schemes such as zero-forcing and conjugate beamforming (with round-robin scheduling) respectively. Furthermore, the results show the superiority of coherent transmission compared to the non-coherent mode under ideal and imperfect CSI for the area-based pilot-reuse factors we consider.
Towards Efficient Point Cloud Graph Neural Networks Through Architectural Simplification
Venue: arXiv cs.CV 2021
Year: 2021

In recent years graph neural network (GNN)-based approaches have become a popular strategy for processing point cloud data, regularly achieving state-of-the-art performance on a variety of tasks. To date, the research community has primarily focused on improving model expressiveness, with secondary thought given to how to design models that can run efficiently on resource constrained mobile devices including smartphones or mixed reality headsets. In this work we make a step towards improving the efficiency of these models by making the observation that these GNN models are heavily limited by the representational power of their first, feature extracting, layer. We find that it is possible to radically simplify these models so long as the feature extraction layer is retained with minimal degradation to model performance; further, we discover that it is possible to improve performance overall on ModelNet40 and S3DIS by improving the design of the feature extractor. Our approach reduces memory consumption by 20$\times$ and latency by up to 9.9$\times$ for graph layers in models such as DGCNN; overall, we achieve speed-ups of up to 4.5$\times$ and peak memory reductions of 72.5%.
DensePASS: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation with Attention-Augmented Context Exchange
Venue: arXiv cs.CV 2021
Year: 2021

Intelligent vehicles clearly benefit from the expanded Field of View (FoV) of the 360-degree sensors, but the vast majority of available semantic segmentation training images are captured with pinhole cameras. In this work, we look at this problem through the lens of domain adaptation and bring panoramic semantic segmentation to a setting, where labelled training data originates from a different distribution of conventional pinhole camera images. First, we formalize the task of unsupervised domain adaptation for panoramic semantic segmentation, where a network trained on labelled examples from the source domain of pinhole camera data is deployed in a different target domain of panoramic images, for which no labels are available. To validate this idea, we collect and publicly release DensePASS - a novel densely annotated dataset for panoramic segmentation under cross-domain conditions, specifically built to study the Pinhole-to-Panoramic transfer and accompanied with pinhole camera training examples obtained from Cityscapes. DensePASS covers both, labelled- and unlabelled 360-degree images, with the labelled data comprising 19 classes which explicitly fit the categories available in the source domain (i.e. pinhole) data. To meet the challenge of domain shift, we leverage the current progress of attention-based mechanisms and build a generic framework for cross-domain panoramic semantic segmentation based on different variants of attention-augmented domain adaptation modules. Our framework facilitates information exchange at local- and global levels when learning the domain correspondences and improves the domain adaptation performance of two standard segmentation networks by 6.05% and 11.26% in Mean IoU.
Post-Quantum Security for Ultra-Reliable Low-Latency Heterogeneous Networks
Venue: arXiv cs.IT 2021
Year: 2021

We consider the problem of post-quantum secure and ultra-reliable communication through a heterogeneous network consisting of multiple connections. Three performance metrics are considered: security, throughput, and in-order delivery delay. In this setting, previous work has looked, individually, at the trade-offs between in-order delivery delay and throughput, and between security and throughput. This is the first work considering the trade-off between all three for heterogeneous communication networks, while taking the computational complexity into account. We present LL-HUNCC, a low latency hybrid universal network coding cryptosystem. LL-HUNCC is an efficient coding scheme which allows for secure communications over a noisy untrusted heterogeneous network by encrypting only a small part of the information being sent. This scheme provides post-quantum security with high throughput and low in-order delivery delay guarantees. We evaluate LL-HUNCC via simulations on a setting inspired by a practical scenario for heterogeneous communications involving a satellite communication link and a 5G communication network. Under this scenario, we compare LL-HUNCC to the state-of-the-art where all communication paths are encrypted via a post-quantum public-key cryptosystem.
VR Sickness Prediction from Integrated HMD's Sensors using Multimodal Deep Fusion Network
Venue: arXiv cs.HC 2021
Year: 2021

Virtual Reality (VR) sickness commonly known as cybersickness is one of the major problems for the comfortable use of VR systems. Researchers have proposed different approaches for predicting cybersickness from bio-physiological data (e.g., heart rate, breathing rate, electroencephalogram). However, collecting bio-physiological data often requires external sensors, limiting locomotion and 3D-object manipulation during the virtual reality (VR) experience. Limited research has been done to predict cybersickness from the data readily available from the integrated sensors in head-mounted displays (HMDs) (e.g., head-tracking, eye-tracking, motion features), allowing free locomotion and 3D-object manipulation. This research proposes a novel deep fusion network to predict cybersickness severity from heterogeneous data readily available from the integrated HMD sensors. We extracted 1755 stereoscopic videos, eye-tracking, and head-tracking data along with the corresponding self-reported cybersickness severity collected from 30 participants during their VR gameplay. We applied several deep fusion approaches with the heterogeneous data collected from the participants. Our results suggest that cybersickness can be predicted with an accuracy of 87.77\% and a root-mean-square error of 0.51 when using only eye-tracking and head-tracking data. We concluded that eye-tracking and head-tracking data are well suited for a standalone cybersickness prediction framework.
The Geometric Structure of Externally Actuated Planar Locomoting Systems in Ambient Media
Venue: arXiv cs.RO 2021
Year: 2021

Robots often interact with the world via attached parts such as wheels, joints, or appendages. In many systems, these interactions, and the manner in which they lead to locomotion, can be understood using the machinery of geometric mechanics, explaining how inputs in the shape space of a robot affect motion in its configuration space and the configuration space of its environment. In this paper we consider an opposite type of locomotion, wherein robots are influenced actively by interactions with an externally forced ambient medium. We investigate two examples of externally actuated systems; one for which locomotion is governed by a principal connection, and is usually considered to possess no drift dynamics, and another for which no such connection exists, with drift inherent in its locomotion. For the driftless system, we develop geometric tools based on previously understood internally actuated versions of the system and demonstrate their use for motion planning under external actuation. For the system possessing drift, we employ nonholonomic reduction to obtain a reduced representation of the system dynamics, illustrate geometric features conducive to studying locomotion, and derive strategies for external actuation.
Fixation and Creativity in Data Visualization Design: Experiences and Perspectives of Practitioners
Venue: arXiv cs.HC 2021
Year: 2021

Data visualization design often requires creativity, and research is needed to understand its nature and means for promoting it. The current visualization literature on creativity is not well developed, especially with respect to the experiences of professional data visualization designers. We conducted semi-structured interviews with 15 data visualization practitioners, focusing on a specific aspect of creativity known as design fixation. Fixation occurs when designers adhere blindly or prematurely to a set of ideas that limit creative outcomes. We present practitioners' experiences and perspectives from their own design practice, specifically focusing on their views of (i) the nature of fixation, (ii) factors encouraging fixation, and (iii) factors discouraging fixation. We identify opportunities for future research related to chart recommendations, inspiration, and perspective shifts in data visualization design.
MatSat: a matrix-based differentiable SAT solver
Venue: arXiv cs.AI 2021
Year: 2021

We propose a new approach to SAT solving which solves SAT problems in vector spaces as a cost minimization problem of a non-negative differentiable cost function J^sat. In our approach, a solution, i.e., satisfying assignment, for a SAT problem in n variables is represented by a binary vector u in {0,1}^n that makes J^sat(u) zero. We search for such u in a vector space R^n by cost minimization, i.e., starting from an initial u_0 and minimizing J to zero while iteratively updating u by Newton's method. We implemented our approach as a matrix-based differential SAT solver MatSat. Although existing main-stream SAT solvers decide each bit of a solution assignment one by one, be they of conflict driven clause learning (CDCL) type or of stochastic local search (SLS) type, MatSat fundamentally differs from them in that it continuously approach a solution in a vector space. We conducted an experiment to measure the scalability of MatSat with random 3-SAT problems in which MatSat could find a solution up to n=10^5 variables. We also compared MatSat with four state-of-the-art SAT solvers including winners of SAT competition 2018 and SAT Race 2019 in terms of time for finding a solution, using a random benchmark set from SAT 2018 competition and an artificial random 3-SAT instance set. The result shows that MatSat comes in second in both test sets and outperforms all the CDCL type solvers.
Extended level set method: a multiphase representation with perfect symmetric property, and its application to multi material topology optimization
Venue: arXiv cs.CE 2021
Year: 2021

This paper provides an extended level set (X-LS) based topology optimiza- tion method for multi material design. In the proposed method, each zero level set of a level set function ϕij represents the boundary between materials i and j. Each increase or decrease of ϕij corresponds to a material change between the two materials. This approach reduces the dependence of the initial configuration in the optimization calculation and simplifies the sensitivity analysis. First, the topology optimization problem is formulated in the X-LS representation. Next, the reaction-diffusion equation that updates the level set function is introduced, and an optimization algorithm that solves the equilibrium equations and the reaction-diffusion equation using the fi- nite element method is constructed. Finally, the validity and utility of the proposed topology optimization method are confirmed using two- and three- dimensional numerical examples.